class_docstr: Module that approximates the Manhattan (city block) distance transform of images using convolutions.
  
  Args:
      kernel_size: size of the convolution kernel.
      h: value that influence the approximation of the min function.
code: def forward(self, image: torch.Tensor) -> torch.Tensor:
      # If images have multiple channels, view the channels in the batch dimension to match kernel shape.
      if image.shape[1] > 1:
          image_in = image.view(-1, 1, image.shape[-2], image.shape[-1])
      else:
          image_in = image
  
      return distance_transform(
          image_in, self.kernel_size, self.h
      ).view_as(image)
  
file_path: kornia/kornia/contrib/distance_transform.py
imports: import math
  import torch
  from torch import nn
  from kornia.filters import filter2d
  from kornia.utils import create_meshgrid
label_desc: Use image node_id 2 for calling the DistanceTransform obj's underlying member method code with example usage: obj.forward(image) and returns: distance_transform